CN115077541A - Positioning method and device for automatic driving vehicle, electronic equipment and storage medium - Google Patents
Positioning method and device for automatic driving vehicle, electronic equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
- G01C21/3804—Creation or updating of map data
- G01C21/3833—Creation or updating of map data characterised by the source of data
- G01C21/3841—Data obtained from two or more sources, e.g. probe vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C22/00—Measuring distance traversed on the ground by vehicles, persons, animals or other moving solid bodies, e.g. using odometers, using pedometers
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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Abstract
The application discloses a positioning method and device for an automatic driving vehicle, an electronic device and a storage medium, wherein the method comprises the following steps: when an automatic driving vehicle is about to enter a preset positioning area, acquiring a laser point cloud map corresponding to the preset positioning area sent by a cloud end, wherein the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data; acquiring current IMU positioning data so as to carry out local filtering on the laser point cloud map to obtain a local laser point cloud map; acquiring current laser point cloud data, and determining the current pose of the laser odometer by combining a local laser point cloud map; and optimizing by using a preset graph optimization algorithm based on the current pose of the laser odometer to obtain the optimized pose so as to perform fusion positioning. The laser point cloud map is located by the aid of the laser point cloud map constructed based on the high-precision inertial navigation data, locating accuracy is high, and locating instantaneity is improved by local filtering processing of the laser point cloud map.
Description
Technical Field
The present disclosure relates to the field of automatic driving technologies, and in particular, to a method and an apparatus for positioning an automatic driving vehicle, an electronic device, and a storage medium.
Background
The synchronous positioning and Mapping (SLAM) technology can accurately realize environment Mapping, positioning and multipoint navigation. Current SLAM technologies can be divided into laser SLAM (lidar SLAM) which uses a laser radar as a sensor, and visual SLAM (visual SLAM) which uses a depth camera. The laser SLAM technology is mature, has few errors and is enough to meet the use requirement of the current environment.
However, a map constructed based on the existing laser SLAM technology is difficult to be applied to vehicle positioning in some special scenes such as tunnels, the positioning accuracy of the map cannot meet the requirement of automatically driving vehicles, and an SLAM positioning scheme based on ICP (Iterative Closest Point algorithm) and NDT (Normal distribution Transform) global matching has low output frequency in the scenes such as tunnels, cannot meet the requirement of real-time positioning, and has poor accuracy.
Disclosure of Invention
The embodiment of the application provides a positioning method and device of an automatic driving vehicle, electronic equipment and a storage medium, so as to improve the positioning accuracy and the positioning real-time performance of the automatic driving vehicle in a specific scene.
The embodiment of the application adopts the following technical scheme:
in a first aspect, an embodiment of the present application provides a positioning method for an autonomous vehicle, where the method includes:
under the condition that an automatic driving vehicle is about to enter a preset positioning area, acquiring a laser point cloud map which is sent by a cloud end and corresponds to the preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
acquiring current IMU positioning data, and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and optimizing by using a preset graph optimization algorithm based on the current pose of the laser odometer to obtain an optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
Optionally, under the condition that the automatic driving vehicle is about to enter the preset positioning area, the laser point cloud map that the acquisition high in the clouds was sent corresponds to the preset positioning area includes:
determining a positioning state of a satellite positioning signal when the autonomous vehicle is about to enter a preset positioning area;
acquiring corresponding inertial navigation positioning data under the condition that the positioning state of the satellite positioning signal is an available state;
and sending the inertial navigation positioning data to a cloud end, and receiving a laser point cloud map of a preset positioning area corresponding to the inertial navigation positioning data sent by the cloud end.
Optionally, the laser point cloud map includes an angular point feature point cloud map and an area feature point cloud map, and the local filtering of the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map includes:
local filtering is carried out on the angular point feature point cloud map according to the current IMU positioning data to obtain an angular point feature local point cloud map;
and carrying out local filtering on the surface feature point cloud map according to the current IMU positioning data to obtain the surface feature local point cloud map.
Optionally, the current IMU positioning data is current longitude and latitude height information calculated by the IMU, and the local filtering of the laser point cloud map according to the current IMU positioning data to obtain the local laser point cloud map includes:
and intercepting the laser point cloud map based on the current longitude and latitude height information to obtain a local laser point cloud map block with a preset size as the local laser point cloud map.
Optionally, the optimizing by using a preset map optimization algorithm based on the current laser odometer pose to obtain an optimized pose includes:
determining factors of a preset map optimization algorithm, wherein the factors comprise at least one of an IMU pre-integration factor and a wheel speed factor;
and optimizing according to the current pose of the laser odometer and the factors of the preset graph optimization algorithm to obtain the optimized pose.
Optionally, before optimizing with a preset map optimization algorithm based on the current laser odometer pose, the method further comprises:
acquiring initial reference point information sent by the cloud, wherein the initial reference point information comprises longitude and latitude height information of an initial reference point and point cloud data;
determining a transformation relation between a laser radar coordinate system and a world coordinate system according to the initial reference point information;
the optimized pose is the pose under a laser radar coordinate system, and the fusion positioning according to the optimized pose to obtain the fusion positioning result of the automatic driving vehicle comprises the following steps:
and transforming the pose under the laser radar coordinate system to the world coordinate system based on the transformation relation between the laser radar coordinate system and the world coordinate system to obtain the pose under the world coordinate system.
Optionally, the optimizing by using a preset map optimization algorithm based on the current laser odometer pose to obtain an optimized pose includes:
acquiring a state identifier of a preset positioning area sent by the cloud;
marking the optimized pose according to the state identifier of the preset positioning area, and performing fusion positioning according to the marked optimized pose.
In a second aspect, an embodiment of the present application further provides a positioning device for an autonomous vehicle, where the device includes:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a laser point cloud map which is sent by a cloud and corresponds to a preset positioning area under the condition that an automatic driving vehicle is about to enter the preset positioning area, the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
the local filtering unit is used for acquiring current IMU positioning data and locally filtering the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
the first determining unit is used for acquiring current laser point cloud data and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and the fusion positioning unit is used for optimizing the pose of the current laser odometer by using a preset map optimization algorithm to obtain the optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
In a third aspect, an embodiment of the present application further provides an electronic device, including:
a processor; and
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform any of the methods described above.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing one or more programs that, when executed by an electronic device including a plurality of application programs, cause the electronic device to perform any of the methods described above.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects: according to the positioning method of the automatic driving vehicle, under the condition that the automatic driving vehicle is about to enter a preset positioning area, a laser point cloud map corresponding to the preset positioning area sent by a cloud end is obtained, and the preset positioning area is an area without available satellite positioning signals; then obtaining current IMU positioning data, and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map; then, acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map; and finally, optimizing by using a preset graph optimization algorithm based on the pose of the current laser odometer to obtain the optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle. According to the positioning method of the automatic driving vehicle, the automatic driving vehicle is positioned by utilizing the laser point cloud map constructed based on the high-precision inertial navigation data, the positioning accuracy is high, and the laser point cloud map is subjected to local filtering processing, so that the positioning real-time performance is greatly improved compared with a global matching mode.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow chart illustrating a method for locating an autonomous vehicle according to an embodiment of the present disclosure;
FIG. 2 is a schematic structural diagram of a positioning device of an autonomous vehicle according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
The embodiment of the present application provides a method for positioning an autonomous vehicle, as shown in fig. 1, which provides a schematic flow chart of the method for positioning an autonomous vehicle in the embodiment of the present application, and the method at least includes the following steps S110 to S140:
step S110, under the condition that an automatic driving vehicle is about to enter a preset positioning area, obtaining a laser point cloud map which is sent by a cloud and corresponds to the preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed based on high-precision inertial navigation data.
The positioning method of the autonomous vehicle according to the embodiment of the application can be executed by a vehicle end, and is mainly directed to real-time positioning of the autonomous vehicle in a preset positioning area, where the preset positioning area refers to an area without available satellite positioning signals, such as a tunnel or other similar positioning scenes. When the automatic driving vehicle is about to enter the preset positioning area, the laser point cloud map corresponding to the preset positioning area is obtained from the cloud end, namely the laser point cloud map for positioning is loaded in advance before the automatic driving vehicle enters the preset positioning area, and therefore subsequent positioning efficiency and positioning real-time performance can be improved.
In addition, in order to ensure the accuracy of the preloaded map, the preloading operation can be executed when the boundary between the current position of the autonomous vehicle and the preset positioning area is less than a certain distance, and the specific distance is set. Of course, the map may be loaded immediately after the autonomous vehicle has traveled to the boundary position of the preset localization area.
The laser point cloud map is obtained by constructing high-precision inertial navigation data acquired by high-precision inertial navigation equipment, and the high-precision inertial navigation factor is constructed through the high-precision inertial navigation data to participate in the optimization process of the preset map optimization algorithm, so that the degradation phenomenon of laser SLAM map construction caused by the small number of feature points in a specific scene is avoided, and the map construction precision of the laser SLAM is improved.
Step S120, obtaining current IMU positioning data, and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map.
The data volume of the laser point cloud map corresponding to the whole preset positioning area is very large, and the matching efficiency is low in the mode of directly utilizing the traditional global matching method based on the ICP or NDP algorithm, so that the embodiment of the application can further obtain the current IMU (Inertial Measurement Unit) positioning data, then utilize the current IMU positioning data to carry out local filtering processing on the laser point cloud map, obtain the local laser point cloud map, reduce the data volume of the map, and improve the matching efficiency and the positioning real-time performance.
And step S130, acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map.
After the local laser point cloud map is obtained, current laser point cloud data can be further obtained, and then feature matching is carried out on the laser point cloud data and the corresponding local laser point cloud map, so that the current laser odometer pose can be determined.
And S140, optimizing the pose of the automatic driving vehicle by using a preset map optimization algorithm based on the current pose of the laser odometer to obtain an optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
Because the laser odometer pose obtained after feature matching has a certain matching error, in order to improve the positioning precision, the pose optimization can be further performed by using a preset map optimization algorithm based on the current laser odometer pose, so that the optimized pose is obtained, and the optimized pose can be input into a Kalman filter or an extended Kalman filter as observation information to perform fusion positioning, so that the fusion positioning result of the automatic driving vehicle is obtained.
According to the positioning method of the automatic driving vehicle, the automatic driving vehicle is positioned by utilizing the laser point cloud map constructed based on the high-precision inertial navigation data, the positioning accuracy is high, and the laser point cloud map is subjected to local filtering processing, so that the positioning real-time performance is greatly improved compared with a global matching mode.
In some embodiments of the present application, the obtaining a laser point cloud map corresponding to a preset positioning area sent by a cloud when an autonomous vehicle is about to enter the preset positioning area includes: determining a positioning state of a satellite positioning signal when the autonomous vehicle is about to enter a preset positioning area; acquiring corresponding inertial navigation positioning data under the condition that the positioning state of the satellite positioning signal is an available state; and sending the inertial navigation positioning data to a cloud end, and receiving a laser point cloud map of a preset positioning area corresponding to the inertial navigation positioning data sent by the cloud end.
According to the embodiment of the application, when the autonomous vehicle is about to enter the preset positioning area, the positioning state of the satellite positioning signal can be determined firstly, because the satellite positioning signal can be generally obtained when the autonomous vehicle is not in the preset positioning area, but the quality of the satellite positioning signal needs to be determined, for example, the satellite positioning signal can be determined according to the RTK (Real-time kinematic) differential state and the satellite number of the obtained satellite positioning signal, and if the differential state is a fixed solution 42 and the satellite number is greater than 20, the current satellite positioning signal can be considered to have better quality and is an available state.
Under the condition that the positioning state of the satellite positioning signal is an available state, corresponding inertial navigation positioning data can be further acquired, and the inertial navigation positioning data can be understood as positioning data with higher precision obtained after IMU positioning data and RTK differential data are fused. The inertial navigation positioning data are sent to the cloud end, so that the cloud end can more accurately determine the current position of the automatic driving vehicle and the preset positioning area where the automatic driving vehicle is to enter, and a laser point cloud map corresponding to the preset positioning area which is constructed in advance can be sent to a vehicle end for subsequent positioning.
In some embodiments of the present application, the laser point cloud map comprises a corner feature point cloud map and a surface feature point cloud map, the local filtering of the laser point cloud map according to the current IMU positioning data comprises: local filtering is carried out on the angular point feature point cloud map according to the current IMU positioning data to obtain an angular point feature local point cloud map; and carrying out local filtering on the surface feature point cloud map according to the current IMU positioning data to obtain the surface feature local point cloud map.
The laser point cloud map constructed in advance in the embodiment of the application is mainly divided into an angular point feature point cloud map constructed based on angular point features and a surface feature point cloud map constructed based on surface features, and when local filtering is carried out, local filtering processing can be respectively carried out on the angular point feature point cloud map and the surface feature point cloud map, so that the angular point feature local point cloud map and the surface feature local point cloud map are obtained. In the subsequent characteristic matching stage, the current laser point cloud data can be respectively matched with the angular point characteristic local point cloud map and the surface characteristic local point cloud map, and then the two matching results are fused to obtain a laser odometer pose.
Certainly, in order to further improve the matching efficiency, in the embodiment of the present application, before the local filtering process is performed, the global filtering may be performed on the angle feature point cloud map and the surface feature point cloud map, that is, the data amount of the point cloud map is reduced on the whole.
In some embodiments of the present application, the current IMU positioning data is current longitude and latitude height information calculated by the IMU, the local filtering is performed on the laser point cloud map according to the current IMU positioning data, and obtaining the local laser point cloud map includes: and intercepting the laser point cloud map based on the current longitude and latitude height information to obtain a local laser point cloud map block with a preset size as the local laser point cloud map.
The current IMU positioning data of the embodiment of the application may specifically refer to current longitude and latitude height information calculated by the IMU, and a local laser point cloud map block with a preset size may be intercepted from a laser point cloud map by using a positioning point corresponding to the longitude and latitude height information as a reference, for example, a local laser point cloud map block with a length, a width and a height of 100m may be intercepted by using a positioning point corresponding to the longitude and latitude height information as a central point based on respective distances of 50m from top to bottom, from left to right and from front to back of the positioning point. Of course, the angular point feature point cloud map and the surface feature point cloud map in the foregoing embodiment may be respectively captured, so as to obtain two local laser point cloud map blocks.
Therefore, local filtering differs from global filtering in that local filtering is to extract a small point cloud map from the global laser point cloud map, the size of the map is changed, while global filtering is to filter the data amount in the global laser point cloud map, and the density of the points is changed.
In addition, it should be noted that although the IMU positioning data itself may have a problem that an accumulated error becomes large over time, since the embodiment of the present application determines an approximate point cloud map block only based on the position of the IMU positioning, the requirement for the position accuracy is relatively low, and the subsequent matching and positioning accuracy is not affected.
In some embodiments of the present application, the optimizing with a preset map optimization algorithm based on the current laser odometer pose, and obtaining an optimized pose includes: determining factors of a preset map optimization algorithm, wherein the factors comprise at least one of an IMU pre-integration factor and a wheel speed factor; and optimizing according to the current pose of the laser odometer and the factors of the preset graph optimization algorithm to obtain the optimized pose.
The preset map optimization algorithm of the embodiment of the application may be implemented in a factor map optimization manner, so that the factors of the preset map optimization algorithm may be determined first, for example, the factors may include an IMU pre-integration factor, a wheel speed factor, and the like. The method comprises the steps of calculating pre-integration through IMU positioning data, obtaining pose transformation, further constructing to obtain IMU pre-integration factors, calculating positions corresponding to all moments based on the speed of the advancing direction acquired by a wheel speed meter in real time, and constructing position constraint based on the position data to further obtain wheel speed factors.
And the pose optimization is carried out by combining one or more factors and the pose of the current laser odometer, so that the optimization efficiency and the optimization effect can be improved, and the positioning precision and the positioning real-time property are further improved.
In some embodiments of the present application, prior to optimizing with a preset map optimization algorithm based on the current laser odometer pose, the method further comprises: acquiring initial reference point information sent by the cloud, wherein the initial reference point information comprises longitude and latitude height information of an initial reference point and point cloud data; determining a transformation relation between a laser radar coordinate system and a world coordinate system according to the initial reference point information; the optimized pose is the pose under a laser radar coordinate system, and the fusion positioning according to the optimized pose to obtain the fusion positioning result of the automatic driving vehicle comprises the following steps: and transforming the pose under the laser radar coordinate system to the world coordinate system based on the transformation relation between the laser radar coordinate system and the world coordinate system to obtain the pose under the world coordinate system.
Because the laser point cloud map constructed in advance in the embodiment of the application is constructed under a laser radar coordinate system, the obtained optimized pose is also under the laser radar coordinate system, and in an automatic driving scene, the absolute position and the pose of a vehicle under a world coordinate system are often required to be determined, so that the optimized pose needs to be converted into the world coordinate system.
Based on this, the initial reference point information can be obtained from the cloud, and the initial reference point can be understood as a positioning point determined before the autonomous driving vehicle enters the preset positioning area, so that the absolute position of the autonomous driving vehicle in the world coordinate system, namely longitude and latitude height information, and the corresponding point cloud data in the laser radar coordinate system can be known, and then the transformation relation of rotation and translation between the laser radar coordinate system and the world coordinate system can be calculated according to the information of the two dimensions, and further the pose in the laser radar coordinate system can be converted into the world coordinate system based on the transformation relation.
In some embodiments of the present application, the optimizing with a preset map optimization algorithm based on the current laser odometer pose, and obtaining an optimized pose includes: acquiring a state identifier of a preset positioning area sent by the cloud; marking the optimized pose according to the state identifier of the preset positioning area, and performing fusion positioning according to the marked optimized pose.
Because the preset positioning area mainly aims at some special positioning scenes, the cloud defines corresponding state identifiers for different preset positioning areas in the map building process, for example, if the preset positioning area is a tunnel scene, the cloud corresponds to a unique identifier of the tunnel positioning area and is used for marking different positioning areas and positioning result sources.
Based on this, in the optimization and fusion positioning stage of the pose, the state identifier defined by the cloud for the preset positioning area can be acquired, and then the positioning result of this time is labeled based on the state identifier, so as to represent the positioning result of this time obtained in which positioning scene. Because the accuracy of the positioning result obtained by the embodiment of the application is higher, compared with the positioning result which is not marked with the state identifier in the same or similar positioning scene, the corresponding confidence coefficient is higher, so that in the fusion positioning stage, the higher confidence coefficient can be given to the positioning result according to the state identifier, and the accuracy and the effect of the fusion positioning are further improved.
In order to facilitate understanding of the embodiments of the present application, a construction process of the laser point cloud map of the present application is further described, which specifically includes: acquiring mapping data acquired by a vehicle end in a preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the mapping data comprises high-precision inertial navigation data and laser point cloud data; post-processing the high-precision inertial navigation data to obtain post-processed high-precision inertial navigation data, wherein the post-processed high-precision inertial navigation data comprises a high-precision inertial navigation pose; constructing factors of a preset graph optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data, wherein the factors of the preset graph optimization algorithm comprise laser odometer factors and high-precision inertial navigation factors; and optimizing the factors based on the preset map optimization algorithm to obtain an optimized pose, and constructing a map according to the optimized pose.
The laser SLAM mapping method can be executed by a cloud end, when the laser SLAM mapping is carried out, mapping data acquired by a vehicle end in a preset positioning area needs to be acquired firstly, the preset positioning area refers to an area where satellite positioning signals are unavailable, and the number of characteristic points in the areas is small. The mapping data mainly comprises high-precision inertial navigation data and laser point cloud data which are acquired in the whole preset positioning area. In order to avoid the degradation phenomenon that appears because the characteristic point quantity that extracts from the laser point cloud data is less under specific scene, the high accuracy inertial navigation positioning data can be gathered to the high accuracy inertial navigation equipment that this application embodiment can utilize car end installation, compares in traditional inertial navigation equipment, can reduce the accumulative error of positioning data grow along with time.
And then, post-processing the high-precision inertial navigation data is needed to further improve the precision of the high-precision inertial navigation data, wherein the post-processing operation is mainly to solve the high-precision inertial navigation data acquired by the high-precision inertial navigation equipment to obtain data such as position, attitude, speed and the like.
According to the method and the device, the pose of the map building can be optimized by adopting a preset map optimization algorithm, and particularly, a factor graph optimization mode can be adopted, so that after the post-processed high-precision inertial navigation data is obtained, corresponding high-precision inertial navigation factors need to be built according to the post-processed high-precision inertial navigation data, and corresponding laser radar odometer factors can be built according to the laser point cloud data and serve as the basis of subsequent optimization. In addition, because the frequencies of the data output by different sensors are different, in order to facilitate subsequent processing, certain time synchronization algorithms such as interpolation can be used for performing time synchronization processing on the post-processed high-precision inertial navigation data and the laser point cloud data.
And optimizing the pose based on the laser odometer factor and the high-precision inertial navigation factor to obtain the optimized pose, and splicing the laser point cloud data based on the optimized pose to obtain the constructed laser point cloud map. By introducing the optimization constraint of the high-precision inertial navigation factor, the degradation phenomenon of the laser odometer factor due to the small number of the characteristic points can be avoided, and the mapping error is reduced.
The laser SLAM mapping process is optimized according to the existing laser SLAM mapping scheme in a specific positioning area, and the optimization process of a preset map optimization algorithm is participated by constructing a high-precision inertial navigation factor, so that the degradation phenomenon of laser SLAM mapping caused by the small number of feature points in a specific scene is avoided, and the mapping precision of the laser SLAM is improved.
In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and constructing a factor of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data includes: performing IMU pre-integration processing based on the IMU data to obtain an IMU pre-integration factor as a factor of the preset map optimization algorithm; the factor based on the preset map optimization algorithm is optimized, and obtaining the optimized pose comprises the following steps: and optimizing based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor to obtain an optimized pose.
The post-processed high-precision inertial navigation data further comprises IMU data, wherein the IMU data can be IMU data without errors such as zero offset, table zero offset and the like, pre-integration can be calculated through the IMU data, pose transformation is obtained, and therefore an IMU pre-integration factor can be constructed. And performing rear-end optimization by taking the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor as factors of a preset map optimization algorithm to obtain an optimized pose, so that the optimization efficiency and the optimization effect are further improved.
In some embodiments of the present application, the optimizing based on the IMU pre-integration factor, the laser odometry factor, and the high-precision inertial navigation factor to obtain the optimized pose includes: adjusting pose confidence coefficients of the IMU pre-integration factor, the laser odometry factor and the high-precision inertial navigation factor based on the high-precision inertial navigation factor; and optimizing according to the pose confidence coefficient of the adjusted IMU pre-integration factor, the pose confidence coefficient of the adjusted laser odometer factor and the pose confidence coefficient of the adjusted high-precision inertial navigation factor to obtain the optimized pose.
When the pose optimization is carried out based on the IMU pre-integration factor, the laser odometer factor and the high-precision inertial navigation factor, due to the fact that the covariance degrees corresponding to different factors are different, the corresponding pose confidence degrees in the optimization process are different, the larger the covariance is, the lower the pose confidence degree is, and the smaller the covariance is, the higher the corresponding pose confidence degree is.
The high-precision inertial navigation factor is constructed based on high-precision inertial navigation data acquired by high-precision inertial navigation equipment, does not depend on the number and abundance of characteristic points extracted from the environment, and does not have the problem of large accumulated error along with time, so that the corresponding covariance is smaller, a higher pose confidence coefficient can be given to the high-precision inertial navigation factor in the pose optimization process, and meanwhile, the pose confidence coefficient corresponding to the IMU pre-integration factor and the laser odometer factor is reduced, namely, compared with the IMU pre-integration factor and the laser odometer factor, the pose corresponding to the high-precision inertial navigation factor is believed with a higher probability.
The process is essentially that in the process of pose optimization, the high-precision inertial navigation factor constructed based on the embodiment adaptively adjusts the covariance size or the confidence degree size of different optimization factors, so that the factor with higher precision occupies higher weight in the optimization process, the factor with lower precision occupies relatively lower weight in the optimization process, and the method is more suitable for the mapping scene with smaller number of feature points such as tunnels and the like.
In addition, it should be noted that, since the covariance size of the IMU pre-integration factor is mainly affected by the time accumulation, the confidence level adjustment for the IMU pre-integration factor may gradually decrease with the time accumulation.
In some embodiments of the present application, the post-processed high-precision inertial navigation data further includes IMU data, and constructing a factor of a preset map optimization algorithm according to the post-processed high-precision inertial navigation data and the laser point cloud data includes: carrying out distortion removal processing on the laser point cloud data by using the IMU data to obtain the laser point cloud data after distortion removal; extracting angular point features and surface features in the undistorted laser point cloud data; and constructing the laser odometry factor according to the extracted corner feature and the extracted surface feature.
When the laser odometer factor is constructed, distortion removal correction processing can be performed on laser point cloud data firstly, specifically, rotation increment can be calculated by utilizing IMU data corresponding to the laser point cloud data of a current frame, translation increment is calculated by utilizing IMU pre-integration, motion distortion correction is performed on laser points of the laser point cloud data at each moment, meanwhile, the pose angle of the IMU data and the pose corresponding to the IMU pre-integration are used for roughly initializing the pose of the laser point cloud data of the current frame, and therefore distortion removal processing of the laser point cloud data is completed.
And then calculating the curvature of each point for the laser point cloud data after motion distortion correction, further extracting angular point features and surface features based on the curvature, and calculating the pose of the laser point cloud data according to the angular point features and the surface features so as to construct a laser odometer factor.
In some embodiments of the present application, before performing optimization based on a factor of the preset map optimization algorithm to obtain an optimized pose, the method further includes: performing loop detection according to the laser point cloud data; and constructing a loop factor according to a loop detection result, wherein the loop factor is used as a factor of the preset graph optimization algorithm.
When the laser SLAM mapping method and device are used for achieving laser SLAM mapping in a specific scene, optimization efficiency and optimization effect can be further improved by adding loop constraints, vehicles can be controlled to run according to loop tracks in the early stage, and the collected mapping data can be used for subsequent loop detection. The loop detection can be specifically carried out by utilizing the existing loop detection algorithm such as a bag-of-words model or a deep learning-based method, so that a pose constraint relation can be established based on a loop detection result, pose optimization is carried out by combining other factors, and the pose optimization efficiency and the pose optimization effect are improved.
In some embodiments of the application, the mapping data further includes wheel speed data, and before optimizing based on factors of the preset map optimization algorithm to obtain an optimized pose, the method further includes: determining position data corresponding to each frame of wheel speed data based on the wheel speed data; and constructing a wheel speed factor according to the position data corresponding to each frame of wheel speed data, wherein the wheel speed factor is used as a factor of the preset map optimization algorithm.
Under the specific map construction scenes such as a tunnel and the like, the wheel speed data acquired based on the wheel speed meter on the vehicle is less influenced by external factors and also has higher precision, so that the wheel speed factor can be further constructed based on the wheel speed data, and richer constraint information is provided for the subsequent pose optimization.
Specifically, in a map building scene, it can be considered that the direction finding speed and the radial speed in the wheel speed data acquired by the wheel speed meter are both 0, that is, only the speed in the advancing direction exists, and since the initial position of the vehicle before entering the preset positioning area is known, the position corresponding to each moment can be calculated based on the speed in the advancing direction acquired by the wheel speed meter in real time, and the position constraint can be constructed based on the position data, so that more reference information is provided for the subsequent optimization process, and the optimization efficiency and the optimization effect are further improved.
In some embodiments of the present application, after performing optimization based on the factors of the preset map optimization algorithm to obtain an optimized pose, and performing map construction according to the optimized pose, the method further includes: comparing the optimized pose with the high-precision inertial navigation pose; determining the mapping precision of the optimized pose according to the comparison result; and returning the mapping precision of the optimized pose to the vehicle end so that the vehicle end can determine whether to rebuild the map according to the mapping precision of the optimized pose.
In order to evaluate the quality of the map construction, after the optimized pose is obtained, the optimized pose used for the map construction can be compared with the high-precision inertial navigation data, and due to the fact that the precision of the high-precision inertial navigation data is high, the pose data corresponding to the high-precision inertial navigation data can be used as a basis for judging the error size of the optimized pose.
The mapping accuracy of the optimized pose can be determined according to the comparison result of the pose and the pose, for example, if the horizontal angle error in the attitude angle is smaller than 0.1, the course angle error is smaller than 0.2, the corresponding statistical error is smaller than twice the standard deviation (2sigma), the position error is smaller than 0.15m, and the corresponding statistical error is smaller than twice the standard deviation (2sigma), the mapping accuracy is considered to be high, the mapping result can be scored in this way, or whether the mapping result meets the preset accuracy requirement or not is represented in other forms, the result is returned to the vehicle end, and the vehicle end can judge whether secondary mapping is needed or not according to the result.
The embodiment of the present application further provides a positioning device 200 for an autonomous vehicle, as shown in fig. 2, which provides a schematic structural diagram of the positioning device for an autonomous vehicle in the embodiment of the present application, where the device 200 at least includes: a first obtaining unit 210, a local filtering unit 220, a first determining unit 230, and a fusion positioning unit 240, wherein:
the first obtaining unit 210 is configured to obtain a laser point cloud map corresponding to a preset positioning area sent by a cloud when an autonomous vehicle is about to enter the preset positioning area, where the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed based on high-precision inertial navigation data;
the local filtering unit 220 is configured to obtain current IMU positioning data, and perform local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
the first determining unit 230 is configured to acquire current laser point cloud data and determine a current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and the fusion positioning unit 240 is configured to optimize the pose of the current laser odometer by using a preset map optimization algorithm to obtain an optimized pose, and perform fusion positioning according to the optimized pose to obtain a fusion positioning result of the autonomous vehicle.
In some embodiments of the present application, the first obtaining unit 210 is specifically configured to: determining a positioning state of a satellite positioning signal when the autonomous vehicle is about to enter a preset positioning area; acquiring corresponding inertial navigation positioning data under the condition that the positioning state of the satellite positioning signal is an available state; and sending the inertial navigation positioning data to a cloud end, and receiving a laser point cloud map of a preset positioning area corresponding to the inertial navigation positioning data sent by the cloud end.
In some embodiments of the present application, the laser point cloud map includes a corner feature point cloud map and a surface feature point cloud map, and the local filtering unit 220 is specifically configured to: local filtering is carried out on the angular point feature point cloud map according to the current IMU positioning data to obtain an angular point feature local point cloud map; and carrying out local filtering on the surface feature point cloud map according to the current IMU positioning data to obtain the surface feature local point cloud map.
In some embodiments of the present application, the current IMU positioning data is current longitude and latitude height information calculated by the IMU, and the local filtering unit 220 is specifically configured to: and intercepting the laser point cloud map based on the current longitude and latitude height information to obtain a local laser point cloud map block with a preset size as the local laser point cloud map.
In some embodiments of the present application, the fusion positioning unit 240 is specifically configured to: determining factors of a preset map optimization algorithm, wherein the factors comprise at least one of an IMU pre-integration factor and a wheel speed factor; and optimizing according to the current pose of the laser odometer and the factors of the preset graph optimization algorithm to obtain the optimized pose.
In some embodiments of the present application, the apparatus further comprises: the second acquisition unit is used for acquiring initial reference point information sent by the cloud end, wherein the initial reference point information comprises longitude and latitude height information of an initial reference point and point cloud data; the second determining unit is used for determining the transformation relation between the laser radar coordinate system and the world coordinate system according to the initial reference point information; the optimized pose is a pose under a laser radar coordinate system, and the fusion positioning unit is specifically configured to: and transforming the pose under the laser radar coordinate system to the world coordinate system based on the transformation relation between the laser radar coordinate system and the world coordinate system to obtain the pose under the world coordinate system.
In some embodiments of the present application, the fusion localization unit 240 is specifically configured to: acquiring a state identifier of a preset positioning area sent by the cloud; marking the optimized pose according to the state identifier of the preset positioning area, and performing fusion positioning according to the marked optimized pose.
It can be understood that the positioning device for an autonomous vehicle can implement the steps of the positioning method for an autonomous vehicle provided in the foregoing embodiments, and the explanations regarding the positioning method for an autonomous vehicle are applicable to the positioning device for an autonomous vehicle, and are not repeated herein.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Referring to fig. 3, at a hardware level, the electronic device includes a processor, and optionally further includes an internal bus, a network interface, and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
And the memory is used for storing programs. In particular, the program may include program code comprising computer operating instructions. The memory may include both memory and non-volatile storage and provides instructions and data to the processor.
The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs the computer program to form the positioning device of the automatic driving vehicle on a logic level. The processor is used for executing the program stored in the memory and is specifically used for executing the following operations:
under the condition that an automatic driving vehicle is about to enter a preset positioning area, acquiring a laser point cloud map which is sent by a cloud end and corresponds to the preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
acquiring current IMU positioning data, and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and optimizing by using a preset graph optimization algorithm based on the current pose of the laser odometer to obtain an optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
The method performed by the positioning device of the autonomous vehicle disclosed in the embodiment of fig. 1 of the present application may be applied to or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The electronic device may further execute the method executed by the positioning apparatus of the autonomous vehicle in fig. 1, and implement the functions of the positioning apparatus of the autonomous vehicle in the embodiment shown in fig. 1, which are not described herein again in this application embodiment.
Embodiments of the present application further provide a computer-readable storage medium storing one or more programs, where the one or more programs include instructions, which when executed by an electronic device including a plurality of application programs, enable the electronic device to perform the method performed by the positioning apparatus for an autonomous vehicle in the embodiment shown in fig. 1, and are specifically configured to perform:
under the condition that an automatic driving vehicle is about to enter a preset positioning area, acquiring a laser point cloud map corresponding to the preset positioning area, wherein the laser point cloud map is sent by a cloud end, the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
acquiring current IMU positioning data, and performing local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and optimizing by using a preset graph optimization algorithm based on the current pose of the laser odometer to obtain an optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of locating an autonomous vehicle, wherein the method comprises:
under the condition that an automatic driving vehicle is about to enter a preset positioning area, acquiring a laser point cloud map which is sent by a cloud end and corresponds to the preset positioning area, wherein the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
acquiring current IMU positioning data, and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
acquiring current laser point cloud data, and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and optimizing by using a preset graph optimization algorithm based on the current pose of the laser odometer to obtain an optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
2. The method of claim 1, wherein the obtaining of the laser point cloud map corresponding to the preset positioning area sent by the cloud end in the case that the autonomous vehicle is about to enter the preset positioning area comprises:
determining a positioning state of a satellite positioning signal when the autonomous vehicle is about to enter a preset positioning area;
acquiring corresponding inertial navigation positioning data under the condition that the positioning state of the satellite positioning signal is an available state;
and sending the inertial navigation positioning data to a cloud end, and receiving a laser point cloud map of a preset positioning area corresponding to the inertial navigation positioning data sent by the cloud end.
3. The method of claim 1, wherein the laser point cloud map comprises a corner feature point cloud map and a surface feature point cloud map, and wherein locally filtering the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map comprises:
local filtering is carried out on the angular point feature point cloud map according to the current IMU positioning data to obtain an angular point feature local point cloud map;
and carrying out local filtering on the surface feature point cloud map according to the current IMU positioning data to obtain the surface feature local point cloud map.
4. The method of claim 1, wherein the current IMU positioning data is current longitude and latitude height information derived by IMU, and the locally filtering the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map comprises:
and intercepting the laser point cloud map based on the current longitude and latitude height information to obtain a local laser point cloud map block with a preset size as the local laser point cloud map.
5. The method of claim 1, wherein the optimizing with a preset map optimization algorithm based on the current laser odometer pose, and the obtaining the optimized pose comprises:
determining factors of a preset map optimization algorithm, wherein the factors comprise at least one of an IMU pre-integration factor and a wheel speed factor;
and optimizing according to the current pose of the laser odometer and the factors of the preset graph optimization algorithm to obtain the optimized pose.
6. The method of claim 1, wherein prior to optimizing with a preset map optimization algorithm based on the current laser odometer pose, the method further comprises:
acquiring initial reference point information sent by the cloud, wherein the initial reference point information comprises longitude and latitude height information of an initial reference point and point cloud data;
determining a transformation relation between a laser radar coordinate system and a world coordinate system according to the initial reference point information;
the optimized pose is the pose under a laser radar coordinate system, and the fusion positioning according to the optimized pose to obtain the fusion positioning result of the automatic driving vehicle comprises the following steps:
and transforming the pose under the laser radar coordinate system to the world coordinate system based on the transformation relation between the laser radar coordinate system and the world coordinate system to obtain the pose under the world coordinate system.
7. The method of claim 1, wherein the optimizing with a preset map optimization algorithm based on the current laser odometer pose comprises:
acquiring a state identifier of a preset positioning area sent by the cloud;
marking the optimized pose according to the state identifier of the preset positioning area, and performing fusion positioning according to the marked optimized pose.
8. A positioning device for an autonomous vehicle, wherein the device comprises:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a laser point cloud map which is sent by a cloud and corresponds to a preset positioning area under the condition that an automatic driving vehicle is about to enter the preset positioning area, the preset positioning area is an area without available satellite positioning signals, and the laser point cloud map is constructed on the basis of high-precision inertial navigation data;
the local filtering unit is used for acquiring current IMU positioning data and carrying out local filtering on the laser point cloud map according to the current IMU positioning data to obtain a local laser point cloud map;
the first determining unit is used for acquiring current laser point cloud data and determining the current laser odometer pose according to the current laser point cloud data and the local laser point cloud map;
and the fusion positioning unit is used for optimizing the pose of the current laser odometer by using a preset map optimization algorithm to obtain the optimized pose, and performing fusion positioning according to the optimized pose to obtain a fusion positioning result of the automatic driving vehicle.
9. An electronic device, comprising:
a processor; and
a memory arranged to store computer executable instructions which, when executed, cause the processor to perform the method of any of claims 1 to 7.
10. A computer readable storage medium storing one or more programs which, when executed by an electronic device comprising a plurality of application programs, cause the electronic device to perform the method of any of claims 1-7.
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| CN115493603A (en) * | 2022-11-17 | 2022-12-20 | 安徽蔚来智驾科技有限公司 | Map alignment method, computer device, and computer-readable storage medium |
| CN116592888A (en) * | 2023-05-08 | 2023-08-15 | 五八智能科技(杭州)有限公司 | Global positioning method, system, device and medium for patrol robot |
| CN116678423A (en) * | 2023-05-26 | 2023-09-01 | 小米汽车科技有限公司 | Multisource fusion positioning method, multisource fusion positioning device and vehicle |
| CN117570973A (en) * | 2023-11-17 | 2024-02-20 | 中科智驰(安庆)智能科技有限公司 | Fusion positioning system and method for multi-scene unmanned vehicle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115493603A (en) * | 2022-11-17 | 2022-12-20 | 安徽蔚来智驾科技有限公司 | Map alignment method, computer device, and computer-readable storage medium |
| CN115493603B (en) * | 2022-11-17 | 2023-03-10 | 安徽蔚来智驾科技有限公司 | Map alignment method, computer device, and computer-readable storage medium |
| CN116592888A (en) * | 2023-05-08 | 2023-08-15 | 五八智能科技(杭州)有限公司 | Global positioning method, system, device and medium for patrol robot |
| CN116678423A (en) * | 2023-05-26 | 2023-09-01 | 小米汽车科技有限公司 | Multisource fusion positioning method, multisource fusion positioning device and vehicle |
| CN116678423B (en) * | 2023-05-26 | 2024-04-16 | 小米汽车科技有限公司 | Multisource fusion positioning method, multisource fusion positioning device and vehicle |
| CN117570973A (en) * | 2023-11-17 | 2024-02-20 | 中科智驰(安庆)智能科技有限公司 | Fusion positioning system and method for multi-scene unmanned vehicle |
| CN117570973B (en) * | 2023-11-17 | 2024-04-26 | 中科智驰(安庆)智能科技有限公司 | Fusion positioning system and method for multi-scene unmanned vehicle |
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